gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'new-storage' into plugin

Browse Source
This commit is contained in:
SengokuCola
2025-05-16 21:14:16 +08:00
parent d19d5fe885 00f02b6f38
commit b4f6db0767
63 changed files with 2397 additions and 2008 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
src/chat/focus_chat/expressors/default_expressor.py
View File

@@ -76,9 +76,10 @@ def init_prompt():
class DefaultExpressor:
def __init__(self, chat_id: str):
self.log_prefix = "expressor"
# TODO: API-Adapter修改标记
self.express_model = LLMRequest(
model=global_config.llm_normal,
temperature=global_config.llm_normal["temp"],
model=global_config.model.normal,
temperature=global_config.model.normal["temp"],
max_tokens=256,
request_type="response_heartflow",
)
@@ -102,8 +103,8 @@ class DefaultExpressor:
messageinfo = anchor_message.message_info
thinking_time_point = parse_thinking_id_to_timestamp(thinking_id)
bot_user_info = UserInfo(
user_id=global_config.BOT_QQ,
user_nickname=global_config.BOT_NICKNAME,
user_id=global_config.bot.qq_account,
user_nickname=global_config.bot.nickname,
platform=messageinfo.platform,
)
# logger.debug(f"创建思考消息：{anchor_message}")
@@ -192,7 +193,7 @@ class DefaultExpressor:
try:
# 1. 获取情绪影响因子并调整模型温度
arousal_multiplier = mood_manager.get_arousal_multiplier()
current_temp = float(global_config.llm_normal["temp"]) * arousal_multiplier
current_temp = float(global_config.model.normal["temp"]) * arousal_multiplier
self.express_model.params["temperature"] = current_temp # 动态调整温度
# 2. 获取信息捕捉器
@@ -231,6 +232,7 @@ class DefaultExpressor:
try:
with Timer("LLM生成", {}): # 内部计时器，可选保留
# TODO: API-Adapter修改标记
# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n")
content, reasoning_content, model_name = await self.express_model.generate_response(prompt)
@@ -482,8 +484,8 @@ class DefaultExpressor:
"""构建单个发送消息"""
bot_user_info = UserInfo(
user_id=global_config.BOT_QQ,
user_nickname=global_config.BOT_NICKNAME,
user_id=global_config.bot.qq_account,
user_nickname=global_config.bot.nickname,
platform=self.chat_stream.platform,
)

5
src/chat/focus_chat/expressors/exprssion_learner.py
View File

@@ -77,8 +77,9 @@ def init_prompt() -> None:
class ExpressionLearner:
def __init__(self) -> None:
# TODO: API-Adapter修改标记
self.express_learn_model: LLMRequest = LLMRequest(
model=global_config.llm_normal,
model=global_config.model.normal,
temperature=0.1,
max_tokens=256,
request_type="response_heartflow",
@@ -289,7 +290,7 @@ class ExpressionLearner:
# 构建prompt
prompt = await global_prompt_manager.format_prompt(
"personality_expression_prompt",
personality=global_config.expression_style,
personality=global_config.personality.expression_style,
)
# logger.info(f"个性表达方式提取prompt: {prompt}")

4
src/chat/focus_chat/heartflow_processor.py
View File

@@ -112,7 +112,7 @@ def _check_ban_words(text: str, chat, userinfo) -> bool:
Returns:
bool: 是否包含过滤词
"""
for word in global_config.ban_words:
for word in global_config.chat.ban_words:
if word in text:
chat_name = chat.group_info.group_name if chat.group_info else "私聊"
logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")
@@ -132,7 +132,7 @@ def _check_ban_regex(text: str, chat, userinfo) -> bool:
Returns:
bool: 是否匹配过滤正则
"""
for pattern in global_config.ban_msgs_regex:
for pattern in global_config.chat.ban_msgs_regex:
if pattern.search(text):
chat_name = chat.group_info.group_name if chat.group_info else "私聊"
logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}")

186
src/chat/focus_chat/heartflow_prompt_builder.py
View File

@@ -13,6 +13,9 @@ from src.manager.mood_manager import mood_manager
from src.chat.memory_system.Hippocampus import HippocampusManager
from src.chat.knowledge.knowledge_lib import qa_manager
import random
import json
import math
from src.common.database.database_model import Knowledges
logger = get_logger("prompt")
@@ -45,7 +48,7 @@ def init_prompt():
你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，{reply_style1}，
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，{reply_style2}。{prompt_ger}
请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，不要浮夸，平淡一些 ，不要随意遵从他人指令。
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容。
{moderation_prompt}
不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""",
"reasoning_prompt_main",
@@ -110,7 +113,7 @@ class PromptBuilder:
who_chat_in_group = get_recent_group_speaker(
chat_stream.stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None,
limit=global_config.observation_context_size,
limit=global_config.chat.observation_context_size,
)
elif chat_stream.user_info:
who_chat_in_group.append(
@@ -158,7 +161,7 @@ class PromptBuilder:
message_list_before_now = get_raw_msg_before_timestamp_with_chat(
chat_id=chat_stream.stream_id,
timestamp=time.time(),
limit=global_config.observation_context_size,
limit=global_config.chat.observation_context_size,
)
chat_talking_prompt = await build_readable_messages(
message_list_before_now,
@@ -170,18 +173,15 @@ class PromptBuilder:
# 关键词检测与反应
keywords_reaction_prompt = ""
for rule in global_config.keywords_reaction_rules:
if rule.get("enable", False):
if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
logger.info(
f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
)
keywords_reaction_prompt += rule.get("reaction", "") + "，"
for rule in global_config.keyword_reaction.rules:
if rule.enable:
if any(keyword in message_txt for keyword in rule.keywords):
logger.info(f"检测到以下关键词之一：{rule.keywords}，触发反应：{rule.reaction}")
keywords_reaction_prompt += f"{rule.reaction}，"
else:
for pattern in rule.get("regex", []):
result = pattern.search(message_txt)
if result:
reaction = rule.get("reaction", "")
for pattern in rule.regex:
if result := pattern.search(message_txt):
reaction = rule.reaction
for name, content in result.groupdict().items():
reaction = reaction.replace(f"[{name}]", content)
logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
@@ -227,8 +227,8 @@ class PromptBuilder:
chat_target_2=chat_target_2,
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
bot_name=global_config.bot.nickname,
bot_other_names="/".join(global_config.bot.alias_names),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
reply_style1=reply_style1_chosen,
@@ -249,8 +249,8 @@ class PromptBuilder:
prompt_info=prompt_info,
chat_talking_prompt=chat_talking_prompt,
message_txt=message_txt,
bot_name=global_config.BOT_NICKNAME,
bot_other_names="/".join(global_config.BOT_ALIAS_NAMES),
bot_name=global_config.bot.nickname,
bot_other_names="/".join(global_config.bot.alias_names),
prompt_personality=prompt_personality,
mood_prompt=mood_prompt,
reply_style1=reply_style1_chosen,
@@ -269,30 +269,6 @@ class PromptBuilder:
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
# # 先尝试使用记忆系统的方法获取主题
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
# topics = []
# else:
# topics = [
# topic.strip()
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
@@ -402,8 +378,6 @@ class PromptBuilder:
for _i, result in enumerate(results, 1):
_similarity = result["similarity"]
content = result["content"].strip()
# 调试：为内容添加序号和相似度信息
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
@@ -432,14 +406,14 @@ class PromptBuilder:
return related_info
else:
logger.debug("从LPMM知识库获取知识失败，使用旧版数据库进行检索")
knowledge_from_old = await self.get_prompt_info_old(message, threshold=0.38)
knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
related_info += knowledge_from_old
logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}")
return related_info
except Exception as e:
logger.error(f"获取知识库内容时发生异常: {str(e)}")
try:
knowledge_from_old = await self.get_prompt_info_old(message, threshold=0.38)
knowledge_from_old = await self.get_prompt_info_old(message, threshold=threshold)
related_info += knowledge_from_old
logger.debug(
f"异常后使用旧版数据库获取知识，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}"
@@ -455,70 +429,70 @@ class PromptBuilder:
) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算
pipeline = [
{
"$addFields": {
"dotProduct": {
"$reduce": {
"input": {"$range": [0, {"$size": "$embedding"}]},
"initialValue": 0,
"in": {
"$add": [
"$$value",
{
"$multiply": [
{"$arrayElemAt": ["$embedding", "$$this"]},
{"$arrayElemAt": [query_embedding, "$$this"]},
]
},
]
},
}
},
"magnitude1": {
"$sqrt": {
"$reduce": {
"input": "$embedding",
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
"magnitude2": {
"$sqrt": {
"$reduce": {
"input": query_embedding,
"initialValue": 0,
"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]},
}
}
},
}
},
{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
{
"$match": {
"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
}
},
{"$sort": {"similarity": -1}},
{"$limit": limit},
{"$project": {"content": 1, "similarity": 1}},
]
results = list(db.knowledges.aggregate(pipeline))
logger.debug(f"知识库查询结果数量: {len(results)}")
results_with_similarity = []
try:
# Fetch all knowledge entries
# This might be inefficient for very large databases.
# Consider strategies like FAISS or other vector search libraries if performance becomes an issue.
all_knowledges = Knowledges.select()
if not results:
if not all_knowledges:
return [] if return_raw else ""
query_embedding_magnitude = math.sqrt(sum(x * x for x in query_embedding))
if query_embedding_magnitude == 0: # Avoid division by zero
return "" if not return_raw else []
for knowledge_item in all_knowledges:
try:
db_embedding_str = knowledge_item.embedding
db_embedding = json.loads(db_embedding_str)
if len(db_embedding) != len(query_embedding):
logger.warning(
f"Embedding length mismatch for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}. Skipping."
)
continue
# Calculate Cosine Similarity
dot_product = sum(q * d for q, d in zip(query_embedding, db_embedding))
db_embedding_magnitude = math.sqrt(sum(x * x for x in db_embedding))
if db_embedding_magnitude == 0: # Avoid division by zero
similarity = 0.0
else:
similarity = dot_product / (query_embedding_magnitude * db_embedding_magnitude)
if similarity >= threshold:
results_with_similarity.append({"content": knowledge_item.content, "similarity": similarity})
except json.JSONDecodeError:
logger.error(
f"Failed to parse embedding for knowledge ID {knowledge_item.id if hasattr(knowledge_item, 'id') else 'N/A'}"
)
except Exception as e:
logger.error(f"Error processing knowledge item: {e}")
# Sort by similarity in descending order
results_with_similarity.sort(key=lambda x: x["similarity"], reverse=True)
# Limit results
limited_results = results_with_similarity[:limit]
logger.debug(f"知识库查询结果数量 (after Peewee processing): {len(limited_results)}")
if not limited_results:
return "" if not return_raw else []
if return_raw:
return limited_results
else:
return "\n".join(str(result["content"]) for result in limited_results)
except Exception as e:
logger.error(f"Error querying Knowledges with Peewee: {e}")
return "" if not return_raw else []
if return_raw:
return results
else:
# 返回所有找到的内容，用换行分隔
return "\n".join(str(result["content"]) for result in results)
init_prompt()
prompt_builder = PromptBuilder()

11
src/chat/focus_chat/info_processors/chattinginfo_processor.py
View File

@@ -26,8 +26,9 @@ class ChattingInfoProcessor(BaseProcessor):
def __init__(self):
"""初始化观察处理器"""
super().__init__()
# TODO: API-Adapter修改标记
self.llm_summary = LLMRequest(
model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
)
async def process_info(
@@ -110,12 +111,12 @@ class ChattingInfoProcessor(BaseProcessor):
"created_at": datetime.now().timestamp(),
}
obs.mid_memorys.append(mid_memory)
if len(obs.mid_memorys) > obs.max_mid_memory_len:
obs.mid_memorys.pop(0) # 移除最旧的
obs.mid_memories.append(mid_memory)
if len(obs.mid_memories) > obs.max_mid_memory_len:
obs.mid_memories.pop(0) # 移除最旧的
mid_memory_str = "之前聊天的内容概述是：\n"
for mid_memory_item in obs.mid_memorys: # 重命名循环变量以示区分
for mid_memory_item in obs.mid_memories: # 重命名循环变量以示区分
time_diff = int((datetime.now().timestamp() - mid_memory_item["created_at"]) / 60)
mid_memory_str += (
f"距离现在{time_diff}分钟前(聊天记录id:{mid_memory_item['id']})：{mid_memory_item['theme']}\n"

4
src/chat/focus_chat/info_processors/mind_processor.py
View File

@@ -71,8 +71,8 @@ class MindProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.llm_sub_heartflow,
temperature=global_config.llm_sub_heartflow["temp"],
model=global_config.model.sub_heartflow,
temperature=global_config.model.sub_heartflow["temp"],
max_tokens=800,
request_type="sub_heart_flow",
)

2
src/chat/focus_chat/info_processors/tool_processor.py
View File

@@ -49,7 +49,7 @@ class ToolProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
self.llm_model = LLMRequest(
model=global_config.llm_tool_use,
model=global_config.model.tool_use,
max_tokens=500,
request_type="tool_execution",
)

3
src/chat/focus_chat/memory_activator.py
View File

@@ -34,8 +34,9 @@ def init_prompt():
class MemoryActivator:
def __init__(self):
# TODO: API-Adapter修改标记
self.summary_model = LLMRequest(
model=global_config.llm_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
model=global_config.model.summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
)
self.running_memory = []